Combined SVM and PCA to Recognize the Brain Function from fMRI Images

摘要：

In this paper, SVM and PCA are incorporated to classify brain fMRI images. This method well overcomes the difficulty of classifying high-dimensional data. PCA is utilized to extract the most representative features. SVM classifier based on selected features is trained to decode brain states. Experimental results show that the proposed method yields good performance. The correct classification rate of our bi-class recognition problems reaches as high as 97％.

关键词： brain function recognition principal component analysis (PCA) support vector machine (SVM) functional magnetic resonance imaging (fMRI) dimension reduction

作者: Guo Rong Xie Song-yun Cheng Xi-na Zhao Hai-tao

作者单位: Department of Electronic and Information Northwestern Polytechnical University Xian,China The First Accessorial Hospital Forth Military Medical University Xian,China

会议类型: 国际会议

会议名称: The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 1-3

在线出版日期: 2009-06-11（万方平台首次上网日期，不代表论文的发表时间）